SimTech EXC 2075 Project Network 3 "Data-integrated model reduction for particles and continua"
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391 to 400 of 793 Results
Data for: Performance of two complementary machine-learned potentials in modelling chemically complex systems
May 26, 2023 - Materials Design
Gubaev, Konstantin; Zaverkin, Viktor; Srinivasan, Prashanth; Duff, Andrew; Kästner, Johannes; Grabowski, Blazej, 2023, "Data for: Performance of two complementary machine-learned potentials in modelling chemically complex systems", https://doi.org/10.18419/DARUS-3516, DaRUS, V1
Data for the publication "Performance of two complementary machine-learned potentials in modelling chemically complex systems", npj. Comp. Mat. This data set contains the datasets of structures in cfg and npz formats INCAR file which was used for VASP calculations python script for reading npz format These are essentially the 2-, 3-, and 4-componen...
CFG_in_distribution_splits.zip
May 26, 2023 - Data for: Performance of two complementary machine-learned potentials in modelling chemically complex systems
ZIP Archive - 70.6 MB - MD5: 53917b372f27e55e617380404c104c2b
training data with 10 cross-validation split, in cfg format
CFG_out_of_distribution.zip
May 26, 2023 - Data for: Performance of two complementary machine-learned potentials in modelling chemically complex systems
ZIP Archive - 201.0 KB - MD5: 99ceed914dda02103fbc6e7c11f110de
testing data in cfg format
INCAR
May 26, 2023 - Data for: Performance of two complementary machine-learned potentials in modelling chemically complex systems
Plain Text - 517 B - MD5: 3481f8c54ebde1c13d6e016ef5b9d493
INCAR file for VASP which was used for calculations
NPZ_in_distribution_splits.zip
May 26, 2023 - Data for: Performance of two complementary machine-learned potentials in modelling chemically complex systems
ZIP Archive - 150.8 MB - MD5: 9bb786377c7162282fa8cbdd11d275da
training data with 10 cross-validation split, in npz format
NPZ_out_of_distribution.zip
May 26, 2023 - Data for: Performance of two complementary machine-learned potentials in modelling chemically complex systems
ZIP Archive - 422.9 KB - MD5: 61b2216e1359cffb4d09edca08980e52
testing data in NPZ format
read_npz.py
May 26, 2023 - Data for: Performance of two complementary machine-learned potentials in modelling chemically complex systems
Python Source Code - 238 B - MD5: 64d14bcaa8283dca5e2c0ed59cb0b573
script which demonstrates the fields in npz files
Data for: Thermodynamic properties on the homologous temperature scale from direct upsampling: Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals
May 12, 2023 - Materials Design
Forslund, Axel; Jung, Jong Hyun; Srinivasan, Prashanth; Grabowski, Blazej, 2023, "Data for: Thermodynamic properties on the homologous temperature scale from direct upsampling: Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals", https://doi.org/10.18419/DARUS-3339, DaRUS, V1
Data for the publication Thermodynamic properties on the homologous temperature scale from direct upsampling: Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals, Phys. Rev. B 107, 174309 (2023). This data set contains - the training sets (VASP files), - the low moment-tensor potentials (MTPs) and high-MTPs, - t...
Gform_vac_Mo_bcc_PBE.txt
May 12, 2023 - Data for: Thermodynamic properties on the homologous temperature scale from direct upsampling: Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals
Plain Text - 151.5 KB - MD5: c3180fa9ce343e958f54113dd7a0c7ae
The Gibbs energy of vacancy formation for bcc molybdenum (Mo) using the PBE functional.
Gform_vac_Ta_bcc_PBE.txt
May 12, 2023 - Data for: Thermodynamic properties on the homologous temperature scale from direct upsampling: Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals
Plain Text - 172.2 KB - MD5: 3bfd25dfb444f69e35d2ae7bc7ae64ae
The Gibbs energy of vacancy formation for bcc molybdenum (Ta) using the PBE functional.
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